The findings of the study, “A Deep Learning Mammography-based Model for Improved Breast Cancer Risk Prediction,” were published in Radiology.
Over the years, researchers attempted to create models incorporating information from genetic and hormonal factors that could estimate the risks a woman has of developing breast cancer. Unfortunately, most of these models failed to accurately predict the chances a patient has of developing the disease.
Mammographic breast density — the ratio between dense mammary tissue and fatty tissue in a woman’s breast assessed during a mammogram — is one of the independent risk factors for breast cancer that has received the most attention. For this reason, it has been added to multiple models to improve their accuracy at predicting breast cancer risk.
“The use of breast density as a proxy for the detailed information embedded on the mammogram is limited because breast density assessment is a subjective assessment and varies widely across radiologists. … [Essentially this means that] same-age patients who are assigned the same density score can have drastically different mammography with vastly different outcomes,” the researchers explained.
For this reason, de